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      The efficacy of social distance and ventilation effectiveness in preventing COVID-19 transmission

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          Highlights

          • Social distancing index P d was developed on basis of exhaled droplet distribution and transmission.

          • The perfect-mixing-based Wells-Riley model was modified by introducing the social distancing index P d and ventilation effectiveness E z .

          • The safe social distance is recommended as 1.6–3.0 m considering aerosol transmission of exhaled large droplets from talking.

          • The modified model used one actual pandemic case to calibrate the infectious dose and verified by a number of other existing cases.

          • Projections using the validated model illustrated the efficacy of social distancing and influence factors on required ventilation rate.

          Abstract

          Social distancing and ventilation were emphasized broadly to control the ongoing pandemic COVID-19 in confined spaces. Rationales behind these two strategies, however, were debated, especially regarding quantitative recommendations. The answers to “what is the safe distance” and “what is sufficient ventilation” are crucial to the upcoming reopening of businesses and schools, but rely on many medical, biological, and engineering factors. This study introduced two new indices into the popular while perfect-mixing-based Wells-Riley model for predicting airborne virus related infection probability – the underlying reasons for keeping adequate social distance and space ventilation. The distance index P d can be obtained by theoretical analysis on droplet distribution and transmission from human respiration activities, and the ventilation index E z represents the system-dependent air distribution efficiency in a space. The study indicated that 1.6−3.0 m (5.2–9.8 ft) is the safe social distance when considering aerosol transmission of exhaled large droplets from talking, while the distance can be up to 8.2 m (26 ft) if taking into account of all droplets under calm air environment. Because of unknown dose response to COVID-19, the model used one actual pandemic case to calibrate the infectious dose (quantum of infection), which was then verified by a number of other existing cases with short exposure time (hours). Projections using the validated model for a variety of scenarios including transportation vehicles and building spaces illustrated that (1) increasing social distance (e.g., halving occupancy density) can significantly reduce the infection rate (20–40 %) during the first 30 min even under current ventilation practices; (2) minimum ventilation or fresh air requirement should vary with distancing condition, exposure time, and effectiveness of air distribution systems.

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          Most cited references25

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          Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy

          In Italy, 128,948 confirmed cases and 15,887 deaths of people who tested positive for SARS-CoV-2 were registered as of 5 April 2020. Ending the global SARS-CoV-2 pandemic requires implementation of multiple population-wide strategies, including social distancing, testing and contact tracing. We propose a new model that predicts the course of the epidemic to help plan an effective control strategy. The model considers eight stages of infection: susceptible (S), infected (I), diagnosed (D), ailing (A), recognized (R), threatened (T), healed (H) and extinct (E), collectively termed SIDARTHE. Our SIDARTHE model discriminates between infected individuals depending on whether they have been diagnosed and on the severity of their symptoms. The distinction between diagnosed and non-diagnosed individuals is important because the former are typically isolated and hence less likely to spread the infection. This delineation also helps to explain misperceptions of the case fatality rate and of the epidemic spread. We compare simulation results with real data on the COVID-19 epidemic in Italy, and we model possible scenarios of implementation of countermeasures. Our results demonstrate that restrictive social-distancing measures will need to be combined with widespread testing and contact tracing to end the ongoing COVID-19 pandemic.
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            Turbulent Gas Clouds and Respiratory Pathogen Emissions: Potential Implications for Reducing Transmission of COVID-19

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              Is Open Access

              Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China

              Intense non-pharmaceutical interventions were put in place in China to stop transmission of the novel coronavirus disease (COVID-19). As transmission intensifies in other countries, the interplay between age, contact patterns, social distancing, susceptibility to infection, and COVID-19 dynamics remains unclear. To answer these questions, we analyze contact surveys data for Wuhan and Shanghai before and during the outbreak and contact tracing information from Hunan Province. Daily contacts were reduced 7-8-fold during the COVID-19 social distancing period, with most interactions restricted to the household. We find that children 0-14 years are less susceptible to SARS-CoV-2 infection than adults 15-64 years of age (odd ratio 0.34, 95%CI 0.24-0.49), while in contrast, individuals over 65 years are more susceptible to infection (odd ratio 1.47, 95%CI: 1.12-1.92). Based on these data, we build a transmission model to study the impact of social distancing and school closure on transmission. We find that social distancing alone, as implemented in China during the outbreak, is sufficient to control COVID-19. While proactive school closures cannot interrupt transmission on their own, they can reduce peak incidence by 40-60% and delay the epidemic.
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                Author and article information

                Contributors
                Journal
                Sustain Cities Soc
                Sustain Cities Soc
                Sustainable Cities and Society
                Elsevier Ltd.
                2210-6707
                2210-6715
                13 July 2020
                November 2020
                13 July 2020
                : 62
                : 102390
                Affiliations
                [a ]School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China
                [b ]Department of Civil, Environmental and Architectural Engineering, University of Colorado, Boulder 80309, USA
                Author notes
                [* ]Corresponding author. john.zhai@ 123456colorado.edu
                Article
                S2210-6707(20)30611-9 102390
                10.1016/j.scs.2020.102390
                7357531
                32834937
                55af6173-cf89-48e0-b919-827f0406318f
                © 2020 Elsevier Ltd. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 11 June 2020
                : 8 July 2020
                : 10 July 2020
                Categories
                Article

                covid-19,social distance,ventilation,infection probability,wells-riley model

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